Gen AI Engineer

Zalway; Mumbai; On-site; INR 400,000 - 700,000; Full time

Benefits offered by this job

Health insurance

Job summary

Zalway is seeking a Principal GenAI Engineer to lead enterprise-scale AI initiatives for Fortune 500 clients. The role focuses on building Graph-powered RAG systems that fuse structured semantic reasoning with advanced LLM architectures for scalable, production-grade AI solutions.

The ideal candidate will bring 8+ years in ML/AI, 2+ years with LLMs, and deep Python/LangChain/LangGraph/SQL proficiency, plus experience deploying GenAI on major cloud platforms.

Qualifications

• 8+ years of experience in ML/AI systems.
• 2+ years hands-on experience with LLMs (RAG, agents, prompt engineering).
• Strong proficiency in Python, LangChain, LangGraph, and SQL.
• Experience deploying GenAI systems on AWS / Azure / GCP.

Responsibilities

• Lead enterprise-scale GenAI implementations for Fortune 500 clients.
• Develop Graph-powered RAG systems combining structured semantic reasoning with LLMs.
• Deliver scalable, production-grade AI solutions with explainability.

Skills

ML/AI systems

LLMs (RAG, agents, prompt engineering)

Python

LangChain

LangGraph

SQL

Cloud deployment (AWS/Azure/GCP)

Tools

Neo4j

Amazon Neptune

Cypher

Job description

We are hiring a Principal GenAI Engineer with strong expertise in LLMs to lead enterprise-scale AI implementations for Fortune 500 clients. This role focuses on building Graph-powered RAG systems (Graph-RAG) that combine structured semantic reasoning with advanced LLM architectures to deliver scalable, explainable, production-grade AI solutions.

What We Are Looking For

• 8+ years of experience in ML/AI systems
• 2+ years hands-on experience with LLMs (RAG, agents, prompt engineering)
• Strong proficiency in Python, LangChain, LangGraph, and SQL
• Experience deploying GenAI systems on AWS / Azure / GCP

Good to Have - Knowledge Graph Expertise

• Design and scale enterprise Knowledge Graph architectures
• Develop ontologies, taxonomies, and semantic data models
• Implement entity resolution, relationship extraction, and graph enrichment
• Experience with Neo4j, Amazon Neptune, or similar graph databases
• Strong hands-on experience with Cypher (or similar graph query languages)
• Build hybrid retrieval systems combining Knowledge Graphs + vector databases
• Integrate structured graph reasoning with LLMs to reduce hallucination and improve explainability